
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "183"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 110 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 105 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.607304	-0.937929	0.064412	2.252345	-0.204633	57.277989	0.259481	0.471748	0.7940	0.7981	0.2444	9.712032	10.605143
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	1.08%	0.271242	-1.538009	0.110626	0.641320	-0.032316	-0.726141	-0.129405	0.414067	0.7398	0.7445	0.2479	2.211098	2.320321
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.734140	-1.145891	0.485523	-0.254360	-0.117517	-0.412482	2.187273	-0.070321	0.0358	0.0292	0.0019	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.527826	-1.360170	5.147411	4.598092	-0.170464	1.484558	0.055677	0.363859	0.6795	0.6951	0.3692	7.703516	8.058867
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.055227	-0.858859	4.207300	4.931450	1.622440	0.256112	0.567613	0.657922	0.6782	0.6935	0.3624	6.450300	6.846891
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.204035	-0.473224	0.003797	0.211187	-0.162415	44.116037	0.185594	0.467180	0.7206	0.7181	0.2763	4.541081	4.521659
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	1.075341	-1.555895	0.857559	0.563452	-0.378391	-0.465054	-0.401708	0.340411	0.7871	0.7704	0.2202	3.277409	2.633619
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	348.690348	354.274190	inf	inf	19.205501	103.071101	154.252716	595.566034	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	7.03%	0.00%	-1.335854	-1.604403	-0.592566	-0.641287	-0.764735	-1.229049	0.280679	2.277076	0.6656	0.6809	0.3606	1.625129	1.595720
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.020160	0.060577	-0.928839	-0.068153	-0.233180	1.646039	1.148548	-0.441743	0.0382	0.0328	0.0012	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.495533	-1.008162	0.492677	0.570402	0.175135	2.450016	8.439742	9.895416	0.6234	0.6329	0.3698	9.556404	8.173569
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	0.00%	-1.830840	-1.944156	-0.548615	-0.273923	0.239157	-0.868663	-0.376613	-0.665189	0.6865	0.6990	0.3786	1.615814	1.654615
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	0.00%	-1.387035	-1.587193	-0.066069	-0.387722	-0.296639	0.104267	-0.746148	-0.572609	0.6867	0.6979	0.3819	1.586940	1.565318
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	0.00%	-2.093955	-2.108591	-0.749585	-0.398439	-0.337250	-0.010162	-0.269810	0.585827	0.6922	0.7026	0.3792	1.641360	1.642777
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-1.552153	-1.557437	0.102814	-0.240378	-0.681647	0.146823	-0.078100	1.855870	0.6864	0.6976	0.3802	1.569154	1.504412
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	96.24%	-1.247965	-1.341822	0.181300	-0.263510	-0.617199	-0.273745	-0.724587	-0.709929	0.6836	0.6919	0.3795	34.917104	39.690584
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.527783	0.004074	-0.101968	0.488570	-1.463294	-0.958863	-0.826037	-0.757404	0.0414	0.0359	0.0017	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	16.76%	4.32%	-0.202977	-1.336717	-0.145131	-0.332108	0.624078	-0.460802	0.031810	-0.070238	0.6740	0.6898	0.3869	1.623586	1.567088
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.044536	-1.359152	-0.420428	-0.759040	0.060043	-1.099444	-0.297287	-0.564799	0.7023	0.7186	0.3795	1.491753	1.531654
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.838225	-0.697025	-0.538433	1.806641	-0.370436	54.973328	-0.564597	-0.371791	0.7603	0.7617	0.3203	4.082122	4.153210
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.164919	-1.531698	-0.723237	-0.746698	-0.131816	-0.826726	-0.504301	2.656151	0.6964	0.7129	0.3726	1.523953	1.488115
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.54%	-1.081480	-0.256968	-0.159462	0.334446	0.026404	0.355728	-0.744599	-0.700819	0.7031	0.7094	0.3732	1.698661	1.650351
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-1.565953	-0.282706	-0.472811	-0.535584	-0.083777	-0.618836	-0.449425	-0.733220	0.7087	0.7155	0.3784	1.458600	1.529188
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.223603	0.858693	-0.079795	0.351224	-0.107701	17.520388	-0.088127	1.311378	0.7774	0.7823	0.2225	4.333943	3.976339
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-1.442672	-1.725597	-0.519480	-0.327310	-0.399309	-0.849849	0.312222	3.564432	0.7005	0.7106	0.3955	1.567226	1.571923
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	1.271803	1.271803	1.199739	1.199739	0.675952	0.675952	0.154089	0.154089	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	-1.383708	-1.066240	-1.463783	-1.027399	0.165457	-0.675741	0.564968	0.633466	0.7167	0.7168	0.3932	1.626247	1.521811
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-1.792720	-1.718654	0.077720	0.383835	-0.368458	0.368990	0.653818	1.845194	0.7133	0.7175	0.3845	1.790812	1.778375
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-1.101741	-0.813048	-0.552523	-0.446277	0.522293	0.078765	0.127242	-0.001094	0.7168	0.7230	0.3801	1.726159	1.567374
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-1.119595	-1.197855	-0.642779	-0.751653	0.680753	-0.560876	-0.352344	-0.033355	0.7103	0.7206	0.3807	1.648197	1.542752
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.927296	-0.872306	-0.485215	-0.669148	0.916763	-0.291905	0.140894	-0.404846	0.7044	0.7181	0.3925	1.645780	1.501607
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-1.431557	-1.017173	-0.736940	-0.685250	0.775766	-0.616125	0.261602	0.224304	0.7064	0.7167	0.3842	1.585061	1.480605
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	13.98%	0.00%	-0.412766	-1.497929	-0.240268	-0.273297	0.859102	-1.089232	-0.160583	-0.472133	0.7080	0.7160	0.3805	1.683944	1.531789
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0379	0.0300	0.0023	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	0.00%	0.007375	-1.554634	0.037324	-0.226513	0.153881	-0.622253	-1.024330	-1.197798	0.6983	0.7109	0.3882	1.712099	1.532198
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7104	0.7215	0.3914	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7093	0.7169	0.3946	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.144479	-1.489146	0.252606	-0.164027	0.309751	115.637750	-0.122780	4.205762	0.7148	0.7078	0.3931	7.550438	8.264714
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.762824	-0.554715	0.172799	0.136112	1.382358	0.208429	0.313353	0.295547	0.7122	0.7183	0.3900	1.721892	1.529570
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0325	0.0289	0.0007	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	3.24%	-0.318934	-1.490572	-0.355533	-0.189466	0.306628	-1.409934	0.689496	1.616651	0.6998	0.7084	0.4017	1.985426	1.690766
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	-1.696498	-1.881727	-0.812806	-0.021922	0.898083	-0.601206	-0.351060	0.096373	0.7552	0.7495	0.3605	1.894712	1.577593
2459541	digital_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	-0.064585	-1.609320	-0.755815	1.406114	1.074074	22.845028	-0.152527	3.975183	0.7412	0.7365	0.3222	4.629692	4.061705
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	-0.901787	-1.117236	0.178423	-0.021937	0.276326	0.988192	0.360207	1.201227	0.7617	0.7563	0.3407	1.564356	1.456437
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	-2.115137	-1.672584	-0.043583	0.488330	0.429841	-0.904468	0.039815	0.481026	0.7423	0.7387	0.4173	1.614998	1.445590
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.625468	-0.943491	0.330162	-0.044472	0.209938	1.590794	-0.150666	-0.517626	0.7941	0.7774	0.3542	1.715593	1.517484
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	-0.967862	-1.487967	-0.294851	-0.902094	0.036741	0.793355	0.110961	-0.430424	0.7743	0.7552	0.3731	1.527252	1.442832
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	-1.819257	-1.774012	-0.038506	-0.740142	1.296844	-0.317575	-0.384916	-0.621213	0.7299	0.7264	0.4042	1.710852	1.556264
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.00%	-1.543376	-1.911784	-0.100097	0.067254	-0.071883	-1.267901	-0.880863	-0.941803	0.7379	0.7353	0.3963	1.590030	1.490723
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-1.177937	-1.288648	-0.498380	-0.681006	0.541042	-0.323977	-0.304522	-0.337742	0.7528	0.7429	0.3919	1.657207	1.557461
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.734605	-1.460712	-0.834964	0.361464	-0.148081	-1.349365	-0.707318	-0.851458	0.9073	0.9327	0.3026	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	14.29%	0.00%	-2.108511	-2.096301	-0.717483	-0.126987	-0.764697	-0.045602	-0.892385	-0.990491	nan	nan	nan	2.192808	1.892595
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-2.039201	-1.745078	0.018811	-0.219275	-1.209608	-0.860647	-0.787732	-0.779014	0.7007	0.6771	0.3472	1.852165	2.059906
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	1.444514	-0.158991	3.388218	-0.887930	40.874642	-0.258236	202.130122	-0.499645	0.0557	0.0364	0.0063	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	-1.154597	-1.414995	-0.516092	-0.724990	1.263716	-1.088324	3.210613	-1.210289	0.9136	0.9406	0.1756	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-1.568279	-1.842571	-0.516616	0.732971	-1.348088	0.145110	0.029265	1.907388	0.8893	0.8520	0.2078	2.569657	1.771613
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	-0.362763	-1.106089	0.184946	-0.012884	-0.188936	0.559110	1.261468	0.053881	0.8680	0.8682	0.4290	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 183 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 183, Node 13:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 183: 2459634

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶